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A human-ai collaborative approach for clinical decision making on rehabilitation assessment

2021Conference object Open access
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dc.contributor.authorLee, Min Hun
dc.contributor.authorSiewiorek, Daniel P. P.
dc.contributor.authorSmailagic, Asim
dc.contributor.authorBernardino, Alexandre
dc.contributor.authorBermúdez i Badia, Sergi
dc.date.accessioned2022-07-21T13:35:05Z
dc.date.available2022-07-21T13:35:05Z
dc.date.issued2021
dc.description.abstractAdvances in artificial intelligence (AI) have made it increasingly applicable to supplement expert’s decision-making in the form of a decision support system on various tasks. For instance, an AI-based system can provide therapists quantitative analysis on patient’s status to improve practices of rehabilitation assessment. However, there is limited knowledge on the potential of these systems. In this paper, we present the development and evaluation of an interactive AI-based system that supports collaborative decision making with therapists for rehabilitation assessment. This system automatically identifies salient features of assessment to generate patient-specific analysis for therapists, and tunes with their feedback. In two evalu ations with therapists, we found that our system supports thera pists significantly higher agreement on assessment (0.71 average F1-score) than a traditional system without analysis (0.66 average F1-score, p < 0.05). After tuning with therapist’s feedback, our sys tem significantly improves its performance from 0.8377 to 0.9116 average F1-scores (p < 0.01). This work discusses the potential of a human-AI collaborative system to support more accurate decision making while learning from each other’s strengthspt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationee, M. H., Siewiorek, D. P., Smailagic, A., Bernardino, A., & Bermúdez i Badia, S. (2021, May). A human-ai collaborative approach for clinical decision making on rehabilitation assessment. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-14).pt_PT
dc.identifier.doi10.1145/3411764.3445472pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/4444
dc.language.isoengpt_PT
dc.publisherACMpt_PT
dc.relationLaboratory of Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectHuman-AI interaction/collaborationpt_PT
dc.subjectDecision support systemspt_PT
dc.subjectExplainable and interactive machine learningpt_PT
dc.subjectPersonalizationpt_PT
dc.subjectStroke rehabilitation assessmentpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleA human-ai collaborative approach for clinical decision making on rehabilitation assessmentpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT
oaire.citation.endPage14pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleProceedings of the 2021 CHI Conference on Human Factors in Computing Systemspt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameBermúdez i Badia
person.givenNameSergi
person.identifier239789
person.identifier.ciencia-idCA17-5E88-2B37
person.identifier.orcid0000-0003-4452-0414
person.identifier.ridC-8681-2018
person.identifier.scopus-author-id6506360007
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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relation.isAuthorOfPublication.latestForDiscoveryef8f1e3b-3c09-4817-80d0-d96aa88051a2
relation.isProjectOfPublicationc0352e81-99e6-4923-99ed-074df09e4db0
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